AI for Science: AI enabled scientific facility transforms fundamental research

Authors

Xiaokang YANG, Artificial Intelligence Institute, Shanghai Jiao Tong University, Shanghai 200240, ChinaFollow
Yanyan XU, Artificial Intelligence Institute, Shanghai Jiao Tong University, Shanghai 200240, China
Lu CHEN, Artificial Intelligence Institute, Shanghai Jiao Tong University, Shanghai 200240, China Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Yunbo WANG, Artificial Intelligence Institute, Shanghai Jiao Tong University, Shanghai 200240, China
Yue GAO, Artificial Intelligence Institute, Shanghai Jiao Tong University, Shanghai 200240, China
Jidong TIAN, Artificial Intelligence Institute, Shanghai Jiao Tong University, Shanghai 200240, China
Kai YU, Artificial Intelligence Institute, Shanghai Jiao Tong University, Shanghai 200240, China Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Yaohui JIN, Artificial Intelligence Institute, Shanghai Jiao Tong University, Shanghai 200240, China
Hong MEI, School of Electronics Engineering and Computer Science, Peking University, Beijing 100871, ChinaFollow

Keywords

AI for Science, generative AI, large language models, scientific facility

Document Type

Vigorously Promote Scientific Research Paradigm Transform

Abstract

In recent years, artificial intelligence (AI) has achieved numerous disruptive breakthroughs in frontier scientific and technological fields, such as AlphaFold2 for protein structure prediction, intelligent control of nuclear fusion, and drug design for COVID-19. These achievements indicate that AI for Science is becoming a new paradigm in research. To achieve fundamental scientific innovation and major technological breakthroughs in the era of intelligence, two core issues should be addressed: 1) how to harness the generality and creativity of the new-generation of AI, especially generative AI and large language models (LLMs), to promote the formation of new paradigms; 2) how to empower and transform traditional scientific facilities using AI. To tackle these challenges, this study proposes a concept of AI-enabled scientific facility (AISF) that caters to the requirements of both establishment of totally new intelligent scientific facility and AI empowerment of existing scientific facilities. It aims to construct an infrastructure system for AI for Science, enabling innovative functionalities such as scientific large language models (LLMs), generative simulation and inversion, autonomous intelligent unmanned experiments, and large-scale trustworthy scientific collaboration. These advancements will accelerate scientific discoveries, synthesis of transformative materials, and application of related engineering technologies.

First page

59

Last Page

69

Language

Chinese

Publisher

Bulletin of Chinese Academy of Sciences

References

1 Jumper J, Evans R, Pritzel A, et al. Highly accurate protein structure prediction with AlphaFold. Nature, 2021, 596: 583-589.

2 Degrave J, Felici F, Buchli J, et al. Magnetic control of tokamak plasmas through deep reinforcement learning. Nature, 2022, 602: 414-419.

3 Zhou Y D, Wang F, Tang J, et al. Artificial intelligence in COVID-19 drug repurposing. The Lancet Digital Health, 2020, 2(12): e667-e676.

4 Coley C W, Thomas III D A, Lummiss J A, et al. A robotic platform for flow synthesis of organic compounds informed by AI planning. Science, 2019, 365: eaax1566.

5 Zhao H T, Chen W, Huang H, et al. A robotic platform for the synthesis of colloidal nanocrystals. Nature Synthesis, 2023, 2(6): 505-514.

6 Zhang L F, Han J Q, Wang H, et al. Deep potential molecular dynamics: A scalable model with the accuracy of quantum mechanics. Physical Review Letters, 2018, 120(14): 143001.

7 Yao Z P, Sánchez-Lengeling B, Bobbitt N S, et al. Inverse design of nanoporous crystalline reticular materials with deep generative models. Nature Machine Intelligence, 2021, 3(1): 76-86.

8 Lee J, Yoon W, Kim S, et al. BioBERT: A pre-trained biomedical language representation model for biomedical text mining. Bioinformatics, 2020, 36(4): 1234-1240.

9 Li Z Y, Kovachki N, Azizzadenesheli K, et al. Fourier neural operator for parametric partial differential equations//International Conference on Learning Representations. Vienna: International Conference on Learning Representations, 2021.

10 Raissi M, Yazdani A, Karniadakis G E. Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations. Science, 2020, 367: 1026-1030.

11 Cranmer M, Sanchez-Gonzalez A, Battaglia P, et al. Discovering symbolic models from deep learning with inductive biases. Advances in Neural Information Processing Systems, 2020, 33: 17429-17442.

12 Guan S Y, Deng H Y, Wang Y B, et al. NeuroFluid: Fluid dynamics grounding with particle-driven neural radiance fields// International Conference on Machine Learning. Baltimore: ACM, 2022: 7919-7929.

13 Ibarz J, Tan J E, Finn C, et al. How to train your robot with deep reinforcement learning: Lessons we have learned. The International Journal of Robotics Research, 2021, 40(4-5): 698-721.

14 罗超然, 马郓, 景翔, 等. 数据空间基础设施的技术挑战及数联网解决方案. 大数据, 2023, 9(2): 110-121. Luo C R, Ma Y, Jing X, et al. Internet of data: A solution for dataspace infrastructure and its technical challenges. Big Data Research, 2023, 9(2): 110-121. (in Chinese)

15 Zhang W, Mei H. A constructive model for collective intelligence. National Science Review, 2020, 7(8): 1273-1277.

Recommended Citation

YANG, Xiaokang; XU, Yanyan; CHEN, Lu; WANG, Yunbo; GAO, Yue; TIAN, Jidong; YU, Kai; JIN, Yaohui; and MEI, Hong (2024) "AI for Science: AI enabled scientific facility transforms fundamental research," Bulletin of Chinese Academy of Sciences (Chinese Version): Vol. 39 : Iss. 1 , Article 8.
DOI: https://doi.org/10.16418/j.issn.1000-3045.20230820004
Available at: https://bulletinofcas.researchcommons.org/journal/vol39/iss1/8